To make the pedestrian detector able to run efficiently directly on computational- and resource-limited devices, we adopt a strategy similar to the one described in ch:counting-on-the-edge, employing, as the backbone of Faster R-CNN, the ResNet50 architecture, a lighter version of the popular ResNet101 [1]}, and reducing the final fully-connected layers responsible for classifying the detected objects since, in our case, we have to localize and identify objects belonging to just one class (i.e., pedestrian). We call Light this modified version of the pedestrian detector module to distinguish it from the Full original one, having instead the ResNet101 backbone and a larger number of fully connected layers. To train this module, we adopt the supervised domain adaptation strategy described in ch:virtual-to-real, where, during the training phase, we mix the synthetic data taken from ViPeD, and the real-world images gathered from the remaining datasets. In this way, we take advantage of the great variability and size of ViPeD, and at the same time, we mitigate the existing domain shift between these synthetic data and the real-world ones. In particular, during the training phase, we exploit batches composed of \(2/3\)  of synthetic images and \(1/3\)  of real-world data, thus considering statistics from both domains throughout the entire procedure and where the real-world data acts as a regularization term over the synthetic data training loss.
